Fine-Tuning ResNet-152V2 for COVID-19 Recognition in Chest Computed Tomography Images
2022 IEEE Information Technologies and Smart Industrial Systems, ITSIS 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-20235124
ABSTRACT
The epidemic Covid-19 has extended to majority of nations. This pandemic is due to a contagious condition 'SARS-CoV-2', was identified by the the International Health association. In order to diagnosis this virus from 2D chest computed tomography (CT) images, we applied three different transfer learning algorithms $VGG-19, ResNet-152V2$ and a Fine-Tuned version of $ResNet-152V2$. The different transfer learning models are used on three hundred and four exams where 74 are normal cases, 60 are community-acquired pneumonia (CAP) cases and 169 were confirmed corona-virus cases. The best accuracy value is reached by the fine-tuned $ResNet-152v2$ by 75% against 70% for the basic $ResNet-152v2$ and 66% for the $VGG-19$. © 2022 IEEE.
Chest Lung CT; Covid-19; Deep learning; Fine tuning; Transfer learning; Computerized tomography; Convolutional neural networks; Diseases; Learning algorithms; Medical imaging; Chest lung computed tomography; Community-acquired pneumonia; Computed tomography images; Condition; International healths; Learning models; Viruses
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 IEEE Information Technologies and Smart Industrial Systems, ITSIS 2022
Year:
2022
Document Type:
Article
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